Are Code Review Processes Influenced by the Genders of the Participants?

TL;DR

This study investigates whether gender biases affect code review outcomes and participation in open-source software communities by analyzing recent data and applying regression models.

Contribution

It conceptually replicates prior studies to assess gender influence on code acceptance, review intervals, and participation using updated datasets and statistical modeling.

Findings

Potential bias in code acceptance rates

Differences in review intervals based on gender

Variation in participation levels

Abstract

Background: Contemporary software development organizations lack diversity and the ratios of women in Free and open-source software (FOSS) communities are even lower than the industry average. Although the results of recent studies hint the existence of biases against women, it is unclear to what extent such biases influence the outcomes of software development tasks. Objective: This study aims to conceptually replicate two recent studies investigating gender biases in FOSS communities \textit{ to identify whether the outcomes of or participation in code reviews (or pull requests) are influenced by the gender of a developer.} In particular, this study focuses on two outcome aspects (i.e., code acceptance, and review interval) and one participation aspect (i.e., code review participation) of code review processes. Method: We will augment the dataset used in the original studies with code reviews /pull requests created during recent years. Using this dataset, we will train multivariate regression models to accurately model the influences of developers' genders on code acceptance, review intervals, and code review participation.